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people-context

people-context

by JinyangWang27
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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record_trait

Store a person's derived trait with a validated category and supporting evidence so agents can retain context on communication style, values, or preferences.

Instructions

Record a derived trait with validated category and provenance.

category is one of communication_style, temperament, values, preference, topics_to_avoid, or other. Cite the observation or interaction it rests on in evidence_note or evidence_ids where you can.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "person_id": "<person_id>",
      "category": "<category>",
      "value": "<value>"
    },
    "name": "record_trait"
  }
}

Input Schema

NameRequiredDescriptionDefault
valueYes
categoryYes
person_idYes
confidenceNo
sensitivityNo
evidence_idsNo
evidence_noteNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.5.0
    • changedOutput schema / (root)
      Previous value: -{
-  "additionalProperties": true,
-  "title": "record_traitDictOutput",
-  "type": "object"
-}New value: +null
  2. First observedv1.1.1

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare a non-readonly, non-idempotent, non-destructive write, and the description adds that the category is validated against a fixed set and that provenance should be attached. It does not say what happens when a trait of the same category already exists for the person, despite the tool being non-idempotent, nor how sensitivity affects storage or later disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short paragraphs with the core action front-loaded and no filler. Enumerating the categories partly duplicates the schema enum, but given the near-zero schema coverage that repetition is defensible rather than wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a seven-parameter write tool with no output schema and no annotations describing mutation semantics, the description covers the action and the most error-prone parameter. Gaps remain around duplicate/conflict behavior and the meaning of `confidence` and `sensitivity`, which an agent would need before calling with non-default values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is effectively 0% for the seven properties, so the description has to compensate. It does explain the six `category` values and the intended role of `evidence_note`/`evidence_ids`, but leaves `person_id`, `value`, `confidence` (scale? bounds?), and `sensitivity` levels entirely undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ("Record a derived trait") plus the qualifying notion of validation and provenance. The word "derived" implicitly separates this from fact/observation/interaction recording, but it never names those siblings, so the distinction must be inferred.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not-to-use guidance, and no routing between this tool and the many adjacent write tools (record_fact, record_observation, record_interaction, correct_record, supersede_fact). The only soft guidance is "cite ... where you can," which concerns provenance rather than tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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